You are viewing documentation for the old version 2.30 of Analyse-it. If you are using version 3.00 or later we recommend you go to the 1-sample t-test
The one sample t-test is a test for a difference in mean between a sample and hypothesised mean. The test is useful when the population standard deviation is unknown preventing use of the z- test.
The requirements of the test are:
- A sample measured on a continuous scale from a population with normal distribution.
Arranging the dataset
Data in existing Excel worksheets can be used and should be arranged in a List dataset layout. The dataset must contain a continuous scale variable.
When entering new data we recommend using New Dataset to create a new 1 variable dataset ready for data entry.
Using the test
To start the test:
- Excel 2007:
Select any cell in the range containing the dataset to analyse, then click Distribution on the Analyse-it tab, then click t-test.
Excel 97, 2000, 2002 & 2003:
Select any cell in the range containing the dataset to analyse, then click Analyse on the Analyse-it toolbar, click Distribution then click t-test.
- Click Variable and select the sample to compare.
- Enter Hypothesised mean to compare the sample mean against.
- Click Alternative hypothesis and select the alternative hypothesis to test:
|X ≠ hypothesised mean to test if the mean is not equal to the hypothesised mean.
|X > hypothesised mean to test if the mean is greater than the hypothesised mean.
|X < hypothesised mean to test if the mean is less than the hypothesised mean.
- Enter Confidence interval to calculate for the mean. The level should be entered as a percentage, between 50 and 100 without the % sign.
- Click OK to run the test.
The report shows the number of observations analysed, number of missing values excluded, summary statistics for the sample, and the hypothesised mean.
The mean of the sample and confidence interval are shown. The confidence interval is the range in which the true population mean is likely to lie with the given probability.
The t-statistic and hypothesis test are shown. The p-value is the probability of rejecting the null hypothesis, that the sample mean is the same as the hypothesised mean, when it is in fact true. A significant p-value implies that the sample mean is different from the hypothesised mean.
Further reading & references
- Handbook of Parametric and Nonparametric Statistical Procedures (3rd edition)
David J. Sheskin, ISBN 1-58488-440-1 2003; 135.
(click to enlarge)